Youth and the Future of Community Forestry
Bibliographic record
Abstract
Forests managed by Indigenous and other local communities generate important benefits for livelihood, and contribute to regional and global biodiversity and carbon sequestration goals. Yet, challenges to community forestry remain. Rural out-migration, for one, can make it hard for communities to maintain broad and diverse memberships invested in local forest commons. This includes young people, who can contribute critical energy, ideas, and skills and are well positioned to take up community forest governance and work, but often aspire to alternative livelihoods and lifestyles. Through an initiative called the Future of Forest Work and Communities, we sought to connect researchers and practitioners with young people living in forest regions, and explore whether community forestry is, or could be, a viable option for them in a globalising world. We achieved this through two phases of qualitative research: youth visioning workshops and questionnaires conducted in 14 forest communities and regions across 9 countries, and a more in-depth case study of two forest communities in Oaxaca, Mexico, using participant observation and semi-structured interviews. We found important synergies across sites. Youth held strong connections with their communities and local forests, but work and/or study aspirations meant many would likely leave their home communities (at least for a time). Community forestry was not seen as an obvious livelihood pathway by a majority of youth, although interest in forest work was evident through participation in several workshop activities. As community leadership and support organisations consider community forestry as an engine of local development, the research highlights the importance of engaging local youth to understand their interests and ideas, and thus identify practical and meaningful ways to empower them as community and territorial actors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".